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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 591–600 of 655 papers

TitleStatusHype
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
A study of Thompson Sampling with Parameter h—0
Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits—0
Asymptotically Optimal Bandits under Weighted Information—0
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget—0
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